Editor/Reviewer loginFrequency : 12 issues per year
Subject : Computer Applications and Technology
ISSN : 2319–8656 (Online)
IJCATR Volume 12 Issue 12
Analysis of Student Performance Correlation Based on BIRCH Clustering Algorithm
Jiaxin Zheng, Wenzao Li, Can Cui, Chengyu Hou
10.7753/IJCATR1212.1002
keywords : Educational Improvement, Data Mining, Association Analysis, BIRCH algorithm, Cluster analysis
Nowadays, how to improve student performance has become a matter of great concern. Therefore, in this paper, a BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) algorithm-based student achievement correlation analysis method is proposed. Firstly, the raw data are preprocessed to eliminate the effect of outliers. Then, the BIRCH algorithm is used to cluster student’s grades, and association rules between student’s grades and course grades are mined according to the clustering results using the adjusted RAND index. The experimental results show that the correlation between student’s daily behavior and final grades is as high as 90%, and the correlation between Advanced Mathematics 1 and Advanced Mathematics 2 is as high as 50%. This method can effectively discover the correlation between student achievement and curriculum, and provide valuable reference information for educators.
@artical{j12122023ijcatr12121002,
Title = "Analysis of Student Performance Correlation Based on BIRCH Clustering Algorithm",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "12",
Issue ="12",
Pages ="6 - 10",
Year = "2023",
Authors ="Jiaxin Zheng, Wenzao Li, Can Cui, Chengyu Hou"}
This paper proposes a performance correlation analysis method based on the BIRCH algorithm.
Correlation analysis of the clustering results is conducted using the Pearson coefficient.
The adjusted Landis index is used for correlation analysis of math course grades.
Visualization results are generated to evaluate the performance of the proposed method.